Despite many association studies of single nucleotide polymorphisms (SNPs) and disease, the genetic basis of most common disease is poorly understood. One possible reason for this is that disease-causing SNPs are evolutionarily disadvantageous, and as a result, a large number of individually rare mutations cause disease. It is difficult to study the relationship between rare genetic variants and common disease using current laboratory and statistical techniques. In order to design optimal studies of rare variants, additional population genetic models, which provide predictions of the correlation, or linkage disequilibrium (LD), between rare and common SNPs are required. Previously developed population genetic models of deleterious variation do not satisfactorily do this. The goal of the proposed research is to develop and test predictions as to the extent of LD between weakly deleterious SNPs. The new models will incorporate selection at multiple sites and realistic models of human demography, both of which are critical for prediction of LD patterns. Since accurate estimates of human demographic history are important for informing the new models, the first Aim of the project will develop novel methods to estimate demographic parameters from genomic data. Specifically, a Markov Chain Monte Carlo approach that efficiently uses genome-wide resequencing data will allow precise parameter estimates from complex population genetic models. This method will be applied to data from human populations generated from the 1000 Genomes Project. Using these parameter estimates, the second Aim will develop realistic population genetic models that can predict the extent of LD around weakly deleterious alleles. This will be done analytically using coalescent theory for simple demographic models. More complex models will require simulation. The third Aim will use data from the third 1000 Genomes Pilot Project to empirically assess patterns of LD around predicted deleterious alleles in different human populations. This work will be critically important for evaluating and designing studies of rare variants and disease risk. Common diseases such as cancer, diabetes and heart disease represent an incredible public health burden. While these diseases are all caused, in part, by genetic risk factors, in many cases, the specific genes involved remain elusive. This research will provide novel insights regarding human evolutionary history which are critical to the design and interpretation studies of genetic variation and risk to common disease.